Data Science, B.S.

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The B.S. program in Data Science will provide students with a strong foundation in the mathematics and basic sciences, fundamental computer programming and algorithmic skills, strong foundations in Data Science fundamentals and methodologies, and a capstone experience. The student will also learn ethical standards related to Data Science. The rigorous training provided by this degree will enable the student to start a career as a Data Scientist in industry, government or community and non-profit organizations. They will also be trained to pursue graduate study in Data Science, Computer Science, and Computer Engineering.

Students will begin the program by building a foundation in mathematics and computer programming and algorithmic techniques. They will then take 4 Data Science CORE courses: Fundamentals of Data Science, Machine Learning, Data Mining, and Database Systems. They will also complete the Ethics for Data Science as part of the CORE. Finally, they will complete 15 credits of elective courses in Data Science and Statistical Learning. The program will conclude with a required Data Science Capstone course in which the student will demonstrate overall knowledge of the discipline by completing a Data Science project, incorporating all the knowledge learned in the courses.

Program Academic Regulations

A minimum grade of C is required in all mathematics, physics, and computer science courses and all 3000-level or above courses that are used to fulfill the undergraduate programs of this department.

As part of the core curriculum, students must receive credit for the two calculus courses: MATH 2211 and MATH 2212 . (When counting the number of semester hours in Areas A, D, and F, only 3 of the 4 credit hours of each calculus course will be counted in Area A and/or D. The fourth hour, or the “rollover hour,” will be counted in Area F.)

Program Degree Requirements

In addition to the Program Degree Requirements, students must fulfill the College of Arts and Sciences Degree Requirements (see section 3030) and the University Degree Requirements (see section 1400).

University Grade-Point Average and Grade Requirements

Georgia State University undergraduate students must achieve an overall institutional grade-point average of 2.0 and a major GPA of 2.0 in Areas G and H to receive a bachelor’s degree from the university. Grades of C- can be used to satisfy graduation requirements. However, some courses have prerequisites that require a grade of C or higher. (See section 1460 for additional information.)

Major Eligibility Requirements

In addition to the general criteria for undergraduate admissions at Georgia State University, to be eligible for the Bachelor of Science in Data Science and to enroll in major-level DSCI courses (DSCI 2720 Data Structures in Python and all 3000- and 4000-level DSCI courses), students must fulfill the following requirements:

Students must earn a 2.5 grade point average across the three course areas (a, b, and c), with no grade lower than a C on the first attempt.
The GPA will be calculated based on the first attempt at the courses listed below at Georgia State University.

The designated pre-major courses are:

DSCI 1301 Principles of Programming for Data Science I;
Either CSC 2510 Theoretical Foundations of Computer Science or MATH 2420 Discrete Mathematics; and
Either MATH 1113 , MATH 2211 , MATH 2212 , or MATH 2215

Where more than one course may be taken toward fulfilling the requirement (items a and b above), the first attempt at the first course taken from the list will be used to calculate the major eligibility grade-point average. For example, in item c, if a student takes MATH 1113 before taking MATH 2211 , then the first attempt at MATH 1113 will be used for the major eligibility GPA.

WFs counts as an attempt. Ws do not count as an attempt. Courses retaken using the university Repeat to Replace policy are not counted as first attempts.

If a student has AP credit for any course designated above, the course will not be used in this GPA calculation, even if the student chooses to take the course.

Transfer students who transfer these course(s) into Georgia State, may use the grades in the transferred course(s) to calculate the GPA or they may attempt them once at Georgia State. Course taken at Georgia State’s Perimeter College count as first-attempts.

Once students are eligible to take major-level Data Science courses (DSCI 2720 and 3000- and 4000-level DSCI courses), they remain eligible to take them as long as they are eligible to enroll at Georgia State University, and they satisfy other Data Science program requirements. Students must meet any prerequisites for the specific 3000- or 4000-level course.

Students in majors other than Data Science may enroll in major-level DSCI courses as long as they meet any pre-requisites for the specific course.

Core Curriculum Areas A-E Requirements and Recommendations

Area A:

Required course: MATH 1113 , or higher-level mathematics course must be taken in Area A.

Area D:

Required course: MATH 2211 Calculus I (4) (or a higher-level mathematics course) (One credit hour counts in Area F or as an elective.)
DSCI students may select a lab sequence from any of the options available to science majors.

Area F: Courses Appropriate to the Major (18)

Students will carry one additional credit hour over to Area F for each 4-credit-hour mathematics course taken in Area A and/or Area D.

Required Courses

DSCI 1301 - Principles of Programming for Data Science I 4 Credit Hours
DSCI 1302 - Principles of Programming for Data Science II 4 Credit Hours
CSC 2510 - Theoretical Foundations of Computer Science 3 Credit Hours
MATH 2212 - Calculus of One Variable II 4 Credit Hours
(unless taken in either Area A or D)

Elective Courses

Select additional elective courses from the following to complete 18 hours in Area F

ACCT 2101 - Principles of Accounting I 3 Credit Hours
ACCT 2102 - Principles of Accounting II 3 Credit Hours
BIOL 1103K - Introductory Biology I 4 Credit Hours
BIOL 1104K - Introductory Biology II 4 Credit Hours
CHEM 1212K - Principles of Chemistry II 4 Credit Hours
CHEM 1211K - Principles of Chemistry I 4 Credit Hours
CHEM 2400 - Organic Chemistry I 3 Credit Hours
ECON 2105 - Principles of Macroeconomics 3 Credit Hours
ECON 2106 - Principles of Microeconomics 3 Credit Hours
MATH 2215 - Multivariate Calculus 4 Credit Hours
PHIL 1010 - Critical Thinking 2 Credit Hours
SCOM 1500 - Public Speaking 3 Credit Hours
SCOM 2300 - Business and Professional Communication 3 Credit Hours
World language at the 2001 or 2002 level

Carryover Hours

Any credit hours exceeding 18 earned to complete the Area F requirements will count toward elective hours.

Area G: Major Requirements (48)

Data Science Preparatory Courses

All courses are required.

CSC 4520 - Design and Analysis of Algorithms 4 Credit Hours
DSCI 2720 - Data Structures in Python for Data Science 3 Credit Hours
MATH 2641 - Linear Algebra I 3 Credit Hours
MATH 3020 - Applied Probability and Statistics for Computer Science 3 Credit Hours

Data Science Core Courses

All courses are required.

DSCI 3000 - Ethics for Data Science 1 Credit Hours
DSCI 4350 - Data Science Capstone 3 Credit Hours
Serves as the CTW (Critical Thinking through Writing) course
DSCI 4710 - Database Systems 4 Credit Hours
DSCI 4740 - Data Mining 4 Credit Hours
DSCI 4780 - Fundamentals of Data Science 4 Credit Hours
DSCI 4850 - Machine Learning 4 Credit Hours

Data Science Electives

For a total of fifteen (15) hours with at least two courses in mathematics, select courses from:

CSC 3320 - System-Level Programming 3 Credit Hours
CSC 4260 - Digital Image Processing 4 Credit Hours
DSCI 4311 - Cloud Computing 4 Credit Hours
DSCI 4370 - Web Programming 4 Credit Hours
DSCI 4750 - Semantic Web 4 Credit Hours
DSCI 4760 - Big Data Programming 4 Credit Hours
DSCI 4810 - Artificial Intelligence 4 Credit Hours
DSCI 4851 - Deep Learning 4 Credit Hours
DSCI 4940 - Data Science Internship 1 to 2 Credit Hours
A maximum of 2 credits of DSCI 4940 course may count towards the electives.
MATH 4211 - Optimization 3 Credit Hours
MATH 4547 - Introduction to Statistical Methods 3 Credit Hours
MATH 4548 - Methods of Regression and Analysis of Variance 3 Credit Hours
Others approved by advisor

Area H: Minor and Additional Courses

Twelve hours of additional courses taken at the 1000-4000 level. Students are encouraged to take courses that focus on a particular domain with data science applications. These hours could also be used to earn a minor in another discipline.

Graduation with Distinction

This unit offers undergraduate students with the opportunity to earn the designation of graduation with distinction in the major. Please contact the undergraduate director for the specific criteria for this honor.